MONITORING & METERING Gaining


meaningful insights from data analysis


Raw sensor data is abundant, but real value lies in smart interpretation says Adrian Barber, who explains how universities are using analytics to align student wellbeing with operational efficiency in modern accommodation.


Adrian Barber prefectcontrols.com


Marketing manager at Prefect Controls


T


Prefect Controls has added new software tools to its Irus ecosystem, enabling data from over 150 sites/75,000 student rooms to inform property managers of how their buildings are performing in comparison with others


machine learning algorithms. It also aligns data from different sources, such as correlating temperature readings with timestamps and room occupancy.


he importance of understanding heating and environmental conditions in student accommodation has


grown significantly. Rising energy costs, increasing awareness of sustainability, and the need to ensure student wellbeing mean providers are turning to data-driven solutions to optimise their living spaces. The challenge lies not in collecting data – modern sensors and smart devices do this continuously – but in extracting meaningful, actionable insights from that data. Student rooms generate a vast


array of data points. Temperature, humidity, sound pressure, CO₂ levels, occupancy patterns, and heating system performance can all be tracked. These data streams provide a real-time snapshot, helping providers ensure comfort, promote energy efficiency, and identify potential maintenance issues before they escalate. However, raw data is rarely immediately useful. For example, a temperature sensor might report fluctuations throughout the day, but without context, it’s unclear whether those changes indicate a problem or are just normal daily cycles. That’s where data analysis and contextualisation come in. Sensors can produce noisy or


incomplete data, perhaps due to connectivity issues or calibration errors. A system such as Irus corrects inconsistencies and fills in missing values using statistical methods and


14


Identifying Patterns The data is analysed to identify trends and anomalies. Time-series analysis helps detect patterns over days, weeks, or even seasons. For instance, if a particular room consistently shows lower temperatures than the rest of the building, it may indicate poor insulation or a malfunctioning heat source. Alternatively, if occupants frequently open windows in winter, it might point to overheating or poor ventilation. Clustering groups of rooms with similar environmental characteristics helps facilities teams prioritise maintenance. And unusual behaviour can be flagged – like higher room temperatures than the system is set to – signifying the use of supplementary heaters.


Combining data To extract truly meaningful information, environmental data


Visual representation highlights specific areas with observations related to environmental conditions and maintenance needs for improved student accommodation management


A dashboard overview displays vital insights on energy usage, room temperature, occupancy, and heating profiles


should be combined with behavioural and usage data. Intelligent thermostats with multi-sensors, can offer a fuller picture. For example, linking low room temperatures


with room absence; this can help differentiate between a technical issue and an intentional energy-saving decision.


Practical applications With the right analysis, institutions can achieve significant outcomes: ● Energy Efficiency: Identifying


overheating zones and optimising heating schedules can reduce energy consumption and costs. ● Improved Comfort: Monitoring


CO₂ levels and ventilation quality ensures students have a healthy indoor environment, which is essential for concentration and wellbeing. ● Preventive Maintenance:


Detecting irregularities in heating systems early allows for proactive maintenance, reducing downtime and costly emergency repairs. ● Informed Planning: Long-term


data trends can inform renovations, retrofits, and even the design of new buildings to meet sustainability goals.


Irus benchmarking New software tools within our Irus ecosystem make sense of data. With more than 75,000 controls across 150 sites, the dataset is of a significant magnitude to return meaningful insight and recommendations for optimising both energy and operational efficiency. This is a real step towards smarter,


more sustainable and student-centred environments. By turning raw sensor data into actionable insights, providers are making evidence-based decisions that improve the student experience and their own efficiencies. The key is in connecting the dots: contextualising, analysing and acting on the data with a clear purpose. ■


EIBI | JULY � AUGUST 2025


Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28  |  Page 29  |  Page 30  |  Page 31  |  Page 32  |  Page 33  |  Page 34  |  Page 35  |  Page 36